Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Spatial Projection of Multiple Climate Variables Using Hierarchical Multitask Learning
Authors: Andre Goncalves, Arindam Banerjee, Fernando Von Zuben
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real climate data show that HMTL produces better results than decoupled MTL methods applied separately on the super-tasks and HMTL significantly outperforms baselines for climate projection. |
| Researcher Affiliation | Collaboration | Andr e R. Gonc alves Center for Research and Development in Telecommunication (CPq D), Brazil EMAIL Arindam Banerjee Dept. of Comp. Sci. & Engg. University of Minnesota, USA EMAIL Fernando J. Von Zuben School of Elec. and Comp. Eng. University of Campinas, Brazil EMAIL |
| Pseudocode | Yes | Algorithm 1: HMTL algorithm. Data: {X}, {Y}. Input: λ0 > 0, λ1 > 0 and λ2 > 0. Result: {Θ}, {Ω}. 2 Ω(t) = Imt, t = 1, ..., T. 3 Θ(t) = U( 0.5, 0.5), t = 1, ..., T. 5 Update {Θ} by solving (5); 6 Update {Ω} by solving (6); 7 until stopping condition met |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is available or provide a link to a repository. |
| Open Datasets | Yes | We collected monthly land temperature and precipitation data of 32 CMIP5 ESMs (Taylor, Stouffer, and Meehl 2012), from 1901 to 2000, in South America. Observed data provided by (Willmott and Matsuura 2001) was used. |
| Dataset Splits | Yes | All the penalization parameters of the methods (λ s in MSSL and HMTL) were chosen by cross-validation. From the training set, we selected the first 80% for training and the next 20% for validation. The best values in the validation set were selected. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper mentions optimization methods like L-BFGS and ADMM, but does not specify any software names with version numbers or other software dependencies. |
| Experiment Setup | Yes | All the penalization parameters of the methods (λ s in MSSL and HMTL) were chosen by cross-validation. ... Using this protocol, the selected parameter values were: S2M2R used λ = 1000; MSSL λ0 = 0.1 and λ1 = 0.1; and HMTL λ0 = 0.1, λ1 = 0.0002, λ2 = 0.01. |